Postdoctoral Research Associate

4w4 weeks ago

University of North Carolina at Chapel Hill

Chapel Hill, US · Full-time · $62,000 – $65,000

About this role

The Zikry Lab at UNC-Chapel Hill is seeking a highly motivated Postdoctoral Research Fellow to join our group at the intersection of statistical methodology, machine learning, and biomedical data science. Our research develops rigorous and interpretable methods for high-dimensional biomedical data, with applications spanning cancer genomics, neuroscience, and precision medicine.

The postdoctoral fellow will play a central role in advancing new statistical and machine learning methodology, focusing on areas such as clustering, dimension reduction, high-dimensional inference, generative modeling, and reliability of ML methods. The fellow will also be responsible for teaching at most one class per semester, an existing 100-level data science course with lecture and evaluation material provided.

This position is within a collaborative, interdisciplinary environment in the School of Data and Information Sciences, with opportunities to work with researchers across the UNC scientific community. Collaborating units include the Computational Medicine Program and the Lineberger Comprehensive Cancer Center.

The postdoctoral fellow will have the opportunity to contribute to cutting-edge research with real-world biomedical impact, building a strong publication record and professional network. This role provides a pathway to independent research careers in academia or industry.

Requirements

Experience in statistical modeling and biomedical data analysis is a plus
Evidence of research productivity (publications, presentations, software) is preferred
Strong background in statistical methodology, machine learning, or biomedical data science
Ability to develop and implement novel computational methods
Proficiency in programming languages commonly used in data science (e.g., R, Python)
Interest in interdisciplinary collaboration across data science and biomedical fields

Responsibilities

Advance new statistical and machine learning methodology for high-dimensional biomedical data
Develop rigorous and interpretable methods in clustering, unsupervised learning, dimension reduction, and manifold learning
Conduct high-dimensional inference and feature selection for biomedical applications
Explore generative modeling and digital twins for biomedical data
Assess and improve the reliability and interpretability of machine learning methods
Teach at most one class per semester (an existing 100-level data science course with provided materials)
Collaborate with researchers across UNC units such as the Computational Medicine Program and Lineberger Comprehensive Cancer Center

Benefits

Opportunity to work in a collaborative, interdisciplinary environment within the School of Data and Information Sciences
Access to resources and collaborations with the Computational Medicine Program and Lineberger Comprehensive Cancer Center
Teaching load of at most one class per semester (or one per academic year based on needs)
Support for professional development as a postdoc at UNC (see UNC postdoc resources)